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  <div class="section" id="numpy-random-generator-exponential">
<h1>numpy.random.Generator.exponential<a class="headerlink" href="#numpy-random-generator-exponential" title="Permalink to this headline">¶</a></h1>
<p>method</p>
<dl class="method">
<dt id="numpy.random.Generator.exponential">
<code class="sig-prename descclassname">Generator.</code><code class="sig-name descname">exponential</code><span class="sig-paren">(</span><em class="sig-param">scale=1.0</em>, <em class="sig-param">size=None</em><span class="sig-paren">)</span><a class="headerlink" href="#numpy.random.Generator.exponential" title="Permalink to this definition">¶</a></dt>
<dd><p>Draw samples from an exponential distribution.</p>
<p>Its probability density function is</p>
<div class="math">
<p><img src="../../../_images/math/e07802bacb29723c1c1503e40bdd2327cc749fe6.svg" alt="f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),"/></p>
</div><p>for <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&gt;</span> <span class="pre">0</span></code> and 0 elsewhere. <img class="math" src="../../../_images/math/7138dad9ac96835665b17f5817eacfcaa9b834c9.svg" alt="\beta"/> is the scale parameter,
which is the inverse of the rate parameter <img class="math" src="../../../_images/math/caf6c43a320d5667458ab8c4df6e51361eb326b3.svg" alt="\lambda = 1/\beta"/>.
The rate parameter is an alternative, widely used parameterization
of the exponential distribution <a class="reference internal" href="#r0dbb9b01ef9c-3" id="id1">[3]</a>.</p>
<p>The exponential distribution is a continuous analogue of the
geometric distribution.  It describes many common situations, such as
the size of raindrops measured over many rainstorms <a class="reference internal" href="#r0dbb9b01ef9c-1" id="id2">[1]</a>, or the time
between page requests to Wikipedia <a class="reference internal" href="#r0dbb9b01ef9c-2" id="id3">[2]</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>scale</strong><span class="classifier">float or array_like of floats</span></dt><dd><p>The scale parameter, <img class="math" src="../../../_images/math/5a7b7992642f388fa92ee087e52d6f5da49b93d4.svg" alt="\beta = 1/\lambda"/>. Must be
non-negative.</p>
</dd>
<dt><strong>size</strong><span class="classifier">int or tuple of ints, optional</span></dt><dd><p>Output shape.  If the given shape is, e.g., <code class="docutils literal notranslate"><span class="pre">(m,</span> <span class="pre">n,</span> <span class="pre">k)</span></code>, then
<code class="docutils literal notranslate"><span class="pre">m</span> <span class="pre">*</span> <span class="pre">n</span> <span class="pre">*</span> <span class="pre">k</span></code> samples are drawn.  If size is <code class="docutils literal notranslate"><span class="pre">None</span></code> (default),
a single value is returned if <code class="docutils literal notranslate"><span class="pre">scale</span></code> is a scalar.  Otherwise,
<code class="docutils literal notranslate"><span class="pre">np.array(scale).size</span></code> samples are drawn.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">ndarray or scalar</span></dt><dd><p>Drawn samples from the parameterized exponential distribution.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r0dbb9b01ef9c-1"><span class="brackets"><a class="fn-backref" href="#id2">1</a></span></dt>
<dd><p>Peyton Z. Peebles Jr., “Probability, Random Variables and
Random Signal Principles”, 4th ed, 2001, p. 57.</p>
</dd>
<dt class="label" id="r0dbb9b01ef9c-2"><span class="brackets"><a class="fn-backref" href="#id3">2</a></span></dt>
<dd><p>Wikipedia, “Poisson process”,
<a class="reference external" href="https://en.wikipedia.org/wiki/Poisson_process">https://en.wikipedia.org/wiki/Poisson_process</a></p>
</dd>
<dt class="label" id="r0dbb9b01ef9c-3"><span class="brackets"><a class="fn-backref" href="#id1">3</a></span></dt>
<dd><p>Wikipedia, “Exponential distribution”,
<a class="reference external" href="https://en.wikipedia.org/wiki/Exponential_distribution">https://en.wikipedia.org/wiki/Exponential_distribution</a></p>
</dd>
</dl>
</dd></dl>

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